Stijn Luca1, Peter Karsmakers2, Kris Cuppens2, Tom Croonenborghs3, Anouk Van de Vel4, Berten Ceulemans5, Lieven Lagae6, Sabine Van Huffel2, Bart Vanrumste2. 1. Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium. Electronic address: stijn.luca@kuleuven.be. 2. Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium; iMinds Future Health Department, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium. 3. Computer Science Department, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium. 4. University Hospital of Antwerp, Wilrijkstraat 10, 2650 Edegem, Belgium. 5. University Hospital of Antwerp, Wilrijkstraat 10, 2650 Edegem, Belgium; Epilepsy Centre for Children and Youth Pulderbos, Reebergenlaan 4, 2242 Zandhoven, Belgium. 6. Epilepsy Centre for Children and Youth Pulderbos, Reebergenlaan 4, 2242 Zandhoven, Belgium; University Hospital Leuven, Herestraat 49, 3000 Leuven, Belgium.
Abstract
OBJECTIVE: Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure detection with the standard method of video electroencephalography monitoring. The goal of this paper is to propose a method for hypermotor seizure detection based on accelerometers that are attached to the extremities. METHODS: Supervised methods that are commonly used in literature need annotation of data and hence require expert (neurologist) interaction resulting in a substantial cost. In this paper an unsupervised method is proposed that uses extreme value statistics and seizure detection based on a model of normal behavior that is estimated using all recorded and unlabeled data. In this way the expensive interaction can be avoided. RESULTS: When applying this method to a labeled dataset, acquired from 7 patients, all hypermotor seizures are detected in 5 of the 7 patients with an average positive predictive value (PPV) of 53%. For evaluating the performance on an unlabeled dataset, seizure events are presented to the system as normal movement events. Since hypermotor seizures are rare compared to normal movements, the very few abnormal events have a negligible effect on the quality of the model. In this way, it was possible to evaluate the system for 3 of the 7 patients when 3% of the training set was composed of seizure events. This resulted in sensitivity scores of 80%, 22% and 90% and a PPV of 89%, 21% and 44% respectively. These scores are comparable with a state-of-the-art supervised machine learning based approach which requires a labeled dataset. CONCLUSIONS: A person-dependent epileptic seizure detection method has been designed that requires little human interaction. In contrast to traditional machine learning approaches, the imbalance of the dataset does not cause substantial difficulties.
OBJECTIVE: Nocturnal home monitoring of epilepticchildren is often not feasible due to the cumbersome manner of seizure detection with the standard method of video electroencephalography monitoring. The goal of this paper is to propose a method for hypermotor seizure detection based on accelerometers that are attached to the extremities. METHODS: Supervised methods that are commonly used in literature need annotation of data and hence require expert (neurologist) interaction resulting in a substantial cost. In this paper an unsupervised method is proposed that uses extreme value statistics and seizure detection based on a model of normal behavior that is estimated using all recorded and unlabeled data. In this way the expensive interaction can be avoided. RESULTS: When applying this method to a labeled dataset, acquired from 7 patients, all hypermotor seizures are detected in 5 of the 7 patients with an average positive predictive value (PPV) of 53%. For evaluating the performance on an unlabeled dataset, seizure events are presented to the system as normal movement events. Since hypermotor seizures are rare compared to normal movements, the very few abnormal events have a negligible effect on the quality of the model. In this way, it was possible to evaluate the system for 3 of the 7 patients when 3% of the training set was composed of seizure events. This resulted in sensitivity scores of 80%, 22% and 90% and a PPV of 89%, 21% and 44% respectively. These scores are comparable with a state-of-the-art supervised machine learning based approach which requires a labeled dataset. CONCLUSIONS: A person-dependent epilepticseizure detection method has been designed that requires little human interaction. In contrast to traditional machine learning approaches, the imbalance of the dataset does not cause substantial difficulties.
Authors: Yun Li; Melanie Alfonzo Horowitz; Jiakang Liu; Aaron Chew; Hai Lan; Qian Liu; Dexuan Sha; Chaowei Yang Journal: Front Public Health Date: 2020-09-30